Categories and curves
Workspace setup:
As we develop more useful models, we’ll begin to practice the art of generating models with multiple estimands. An estimand is a quantity we want to estimate from the data. Our models may not themselves produce the answer to our central question, so we need to know how to calculate these values from the posterior distributions.
This is going to be different from prior regression courses (PSY 612), where our models were often designed to give us precisely what we wanted. For example, consider the regression:
\[ \hat{Y} = b_0 + b_1(D) \] Where \(Y\) is a continuous outcome and \(D\) is a dummy coded variable (0 = control; 1 = treatment).
Forget dummy codes. From here on out, we will incorporate categorical causes into our models by using index variables. An index variable contains integers that correspond to different categories. The numbers have no inherent meaning – rather, they stand as placeholders or shorthand for categories.
Let’s write a mathematical model to express weight in terms of sex.
\[\begin{align*} w_i &\sim \text{Normal}(\mu_i, \sigma) \\ \mu_i &= \alpha_{SEX[i]} \\ \alpha_j &\sim \text{Normal}(60, 20)\text{ for }j = 1..2 \\ \sigma &\sim \text{Uniform}(0, 50) \end{align*}\]
quap() mean sd 5.5% 94.5%
a[1] 41.821522 0.4013652 41.18006 42.462981
a[2] 48.595487 0.4272742 47.91262 49.278354
sigma 5.489687 0.2069006 5.15902 5.820354
Here, we are given the estimates of the parameters specified in our model: the average weight of women (a[1]) and the average weight of men (a[2]). But our question is whether these average weights are different. How do we get that?
List of 2
$ sigma: num [1:10000] 5.5 5.37 5.48 5.4 5.53 ...
$ a : num [1:10000, 1:2] 41.2 42 42.3 42.2 42.4 ...
- attr(*, "source")= chr "quap posterior: 10000 samples from m1"
[,1] [,2]
[1,] 41.21299 48.71478
[2,] 42.02449 48.60198
[3,] 42.34086 49.55100
[4,] 42.22376 49.24485
[5,] 42.41163 49.34327
[6,] 41.48790 48.16697
mean sd 5.5% 94.5% histogram
sigma 5.493076 0.2073966 5.161443 5.829173 ▁▁▁▅▇▅▁▁▁
a[1] 41.818507 0.4016983 41.172278 42.456285 ▁▁▃▇▅▁▁
a[2] 48.594457 0.4284646 47.903976 49.278449 ▁▁▅▇▂▁▁
diff_fm -6.775950 0.5837513 -7.691804 -5.834095 ▁▁▂▅▇▅▂▁▁▁
We can create two plots. One is the posterior distributions of average female and male weights and one is the average difference.
p1 <- post %>% as.data.frame() %>%
pivot_longer(starts_with("a")) %>%
mutate(sex = ifelse(name == "a.1", "female", "male")) %>%
ggplot(aes(x=value, color = sex)) +
geom_density(linewidth = 2) +
labs(x = "weight(kg)")
p2 <- post %>% as.data.frame() %>%
ggplot(aes(x=diff_fm)) +
geom_density(linewidth = 2) +
labs(x = "difference in weight(kg)")
( p1 | p2)A note that the distributions of the mean weights is not the same as the distribution of weights period. For that, we need the posterior predictive distributions.
pred_f <- rnorm(1e4, mean = post$a[,1], sd = post$sigma )
pred_m <- rnorm(1e4, mean = post$a[,2], sd = post$sigma )
pred_post = data.frame(pred_f, pred_m) %>%
mutate(diff = pred_f-pred_m)
# plot distributions
p1 <- pred_post %>% pivot_longer(starts_with("pred")) %>%
mutate(sex = ifelse(name == "pred_f", "female", "male")) %>%
ggplot(aes(x = value, color = sex)) +
geom_density(linewidth = 2) +
labs(x = "weight (kg)")
# plot difference
# Compute density first
density_data <- density(pred_post$diff)
# Convert to a tibble for plotting
density_df <- tibble(
x = density_data$x,
y = density_data$y,
fill_group = ifelse(x < 0, "male", "female") # Define fill condition
)
# Plot with area fill
p2 <- ggplot(density_df, aes(x = x, y = y, fill = fill_group)) +
geom_area() + # Adjust transparency if needed
geom_line(linewidth = 1.2, color = "black") + # Keep one continuous curve
labs(x = "Difference in weight (F-M)", y = "density") +
guides(fill = "none")
(p1 | p2)In the rethinking package, the dataset milk contains information about the composition of milk across primate species, as well as some other facts about those species. The taxonomic membership of each species is included in the variable clade; there are four categories.
kcal.per.g). 1'data.frame': 29 obs. of 8 variables:
$ clade : Factor w/ 4 levels "Ape","New World Monkey",..: 4 4 4 4 4 2 2 2 2 2 ...
$ species : Factor w/ 29 levels "A palliata","Alouatta seniculus",..: 11 8 9 10 16 2 1 6 28 27 ...
$ kcal.per.g : num 0.49 0.51 0.46 0.48 0.6 0.47 0.56 0.89 0.91 0.92 ...
$ perc.fat : num 16.6 19.3 14.1 14.9 27.3 ...
$ perc.protein : num 15.4 16.9 16.9 13.2 19.5 ...
$ perc.lactose : num 68 63.8 69 71.9 53.2 ...
$ mass : num 1.95 2.09 2.51 1.62 2.19 5.25 5.37 2.51 0.71 0.68 ...
$ neocortex.perc: num 55.2 NA NA NA NA ...
\[\begin{align*} K_i &\sim \text{Normal}(\mu_i, \sigma) \\ \mu_i &= \alpha_{\text{CLADE}[i]} \\ \alpha_i &\sim \text{Normal}(0, 0.5) \text{ for }j=1..4 \\ \sigma &\sim \text{Exponential}(1) \\ \end{align*}\]
Exercise: Now fit your model using quap(). It’s ok if your mathematical model is a bit different from mine.
flist <- alist(
K ~ dnorm( mu , sigma ) ,
mu <- a[clade_id] ,
a[clade_id] ~ dnorm( 0 , 0.5 ) ,
sigma ~ dexp( 1 )
)
m2 <- quap(
flist, data = milk
)
precis( m2, depth=2 ) mean sd 5.5% 94.5%
a[1] -0.4843490 0.21764100 -0.83218136 -0.1365166
a[2] 0.3662533 0.21705871 0.01935159 0.7131551
a[3] 0.6752244 0.25753374 0.26363578 1.0868131
a[4] -0.5858103 0.27450875 -1.02452828 -0.1470923
sigma 0.7196446 0.09653315 0.56536594 0.8739232
rethinkingPlot the following distributions:
post <- extract.samples( m2 )
names(labels) = paste("a.", 1:4, sep = "")
post %>% as.data.frame() %>%
pivot_longer(starts_with("a")) %>%
mutate(name = recode(name, !!!labels)) %>%
ggplot(aes(x = value, color = name)) +
geom_density(linewidth = 2) +
labs(title = "Posterior distribution of expected milk energy")post <- extract.samples( m2 )
a.1 = rnorm(1e4, post$a[,1], post$sigma)
a.2 = rnorm(1e4, post$a[,2], post$sigma)
a.3 = rnorm(1e4, post$a[,3], post$sigma)
a.4 = rnorm(1e4, post$a[,4], post$sigma)
data.frame(a.1, a.2, a.3, a.4) %>%
pivot_longer(everything()) %>%
mutate(name = recode(name, !!!labels)) %>%
ggplot(aes(x = value, color = name)) +
geom_density(linewidth = 2) +
labs(title = "Posterior distribution of predicted milk energy")Let’s return to the weight example. What if we want to control for height?
\[\begin{align*} w_i &\sim \text{Normal}(\mu_i, \sigma) \\ \mu_i &= \alpha_{S[i]} + \beta_{S[i]}(H_i-\bar{H})\\ \alpha_j &\sim \text{Normal}(60, 20)\text{ for }j = 1..2 \\ \beta_j &\sim \text{Normal}(0, 5)\text{ for }j = 1..2 \\ \sigma &\sim \text{Uniform}(0, 50) \end{align*}\]
mean sd 5.5% 94.5%
a[1] 45.1625169 0.43768064 44.4630187 45.8620151
a[2] 45.0558274 0.45652955 44.3262050 45.7854498
b[1] 0.6579586 0.06096247 0.5605288 0.7553884
b[2] 0.6141766 0.05493322 0.5263827 0.7019705
sigma 4.2279306 0.15934635 3.9732644 4.4825969
List of 3
$ sigma: num [1:10000] 4.25 4.28 4.33 4.29 4.23 ...
$ a : num [1:10000, 1:2] 44.9 45.8 45.4 45 44.2 ...
$ b : num [1:10000, 1:2] 0.635 0.665 0.642 0.65 0.585 ...
- attr(*, "source")= chr "quap posterior: 10000 samples from m3"
Plot the slopes using extract.samples()
xbar = mean(d$height) # need this because we centered
post <- extract.samples(m3) # sample intercepts and slopes from the posterior
plot(d$weight ~ d$height, cex=0.5, pch=16, col=col.alpha("darkgrey",0.5),
xlab = "height", ylab = "weight")
#plot the lines implied by the first 50 draws from the posterior
for(i in 1:50){
curve(post$a[i, 1] +post$b[i, 1]*(x-xbar),
add = T,
col=col.alpha("#1c5253",0.1))
curve(post$a[i, 2] +post$b[i, 2]*(x-xbar),
add = T,
col=col.alpha("#e07a5f",0.1))
}Plot the slopes using link(). (Run this yourself and open up the objects muF and muM to determine what the link() function is doing.)
xseq <- seq( min(d$height), max(d$height), len=100) # some values for X
plot(d$weight ~ d$height, cex=0.5, pch=16, col=col.alpha("darkgrey",0.3),
xlim = range(d$height), ylim = range(d$weight),
xlab = "height", ylab = "weight")
muF <- link(m3, data=list(sex=rep(1,100), height=xseq, Hbar = mean(d$height)))
lines(xseq, apply(muF, 2, mean), lwd = 2, col = "#1c5253" )
muM <- link(m3, data=list(sex=rep(2,100), height=xseq, Hbar = mean(d$height)))
lines(xseq, apply(muM, 2, mean), lwd = 2, col = "#e07a5f")Return to the milk data. Write a mathematical model expressing the energy of milk as a function of the species body mass (mass) and clade category. Be sure to include priors. Fit your model using quap().
\[\begin{align*} K_i &\sim \text{Normal}(\mu_i, \sigma) \\ \mu_i &= \alpha_{\text{CLADE}[i]} + \beta_{\text{CLADE}[i]}(M-\bar{M})\\ \alpha_i &\sim \text{Normal}(0, 0.5) \text{ for }j=1..4 \\ \beta_i &\sim \text{Normal}(0, 0.5) \text{ for }j=1..4 \\ \sigma &\sim \text{Exponential}(1) \\ \end{align*}\]
dat <- list(
K = standardize(milk$kcal.per.g),
M = milk$mass,
Mbar = mean(milk$mass),
clade_id = milk$clade_id
)
flist <- alist(
K ~ dnorm( mu , sigma ) ,
mu <- a[clade_id] +b[clade_id]*(M-Mbar),
a[clade_id] ~ dnorm( 0 , 0.5 ) ,
b[clade_id] ~ dnorm( 0 , 0.5 ) ,
sigma ~ dexp( 1 )
)
m4 <- quap(
flist, data = dat
) mean sd 5.5% 94.5%
a[1] -0.434265349 0.261120708 -0.851586674 -0.016944025
a[2] -0.282787425 0.478554675 -1.047610223 0.482035373
a[3] 0.368828332 0.418886926 -0.300633881 1.038290544
a[4] -0.005062219 0.498109194 -0.801136917 0.791012478
b[1] -0.002670563 0.007183379 -0.014150989 0.008809864
b[2] -0.061303449 0.040137375 -0.125450727 0.002843829
b[3] -0.047926260 0.050277984 -0.128280190 0.032427670
b[4] 0.064916555 0.046232453 -0.008971834 0.138804943
sigma 0.692517697 0.092027095 0.545440625 0.839594769
xseq <- seq( min(milk$mass), max(milk$mass), len=100)
Mbar = mean(milk$mass)
custom_colors = c("#1c5253", "#e07a5f", "#f2cc8f", "#81b29a")
colors = custom_colors[milk$clade_id]
plot(milk$K ~ milk$mass, col = colors,
pch = 16,
xlim = range(milk$mass), ylim = range(milk$K),
xlab = "height", ylab = "weight")
mu1 <-
link(m4, data=list(clade_id=rep(1,100), M=xseq, Mbar = Mbar))
lines(xseq, apply(mu1, 2, mean), lwd = 2, col = "#1c5253" )
mu2 <-
link(m4, data=list(clade_id=rep(2,100), M=xseq, Mbar = Mbar))
lines(xseq, apply(mu2, 2, mean), lwd = 2, col = "#e07a5f" )
mu3 <-
link(m4, data=list(clade_id=rep(3,100), M=xseq, Mbar = Mbar))
lines(xseq, apply(mu3, 2, mean), lwd = 2, col = "#f2cc8f" )
mu4 <-
link(m4, data=list(clade_id=rep(4,100), M=xseq, Mbar = Mbar))
lines(xseq, apply(mu4, 2, mean), lwd = 2, col = "#81b29a" )
legend("topright", legend = levels(milk$clade),
col = custom_colors, pch = 16)